Understanding Better the Influential Factors of Commuters’ Multi-Day Travel Behavior: Evidence from Shanghai, China

Abstract

Alleviating traffic congestion and developing sustainable transportation systems in a city can be assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transport. Strategies for promoting these desirable transportation modes can be identified based on a sound understanding of how commuters choose travel modes. In this study, multi-day commuting travel mode data was used to explore factors that influenced commute mode choice. A multinomial logit model and a binary logit model were proposed to study commuter travel behavior. The results showed the following. (1) Age, gender, and marriage indirectly influence the commute mode choice; (2) The cost of travel mode has little effect on commute mode choice; (3) The probability of commute mode change mainly influences the car mode choice; (4) The number of transfer times and the distance to the nearest public transport stations are main factors that restrict commuters from choosing public transport; (5) The number of bicycles in the family and commute distance are main factors that restrict commuters from choosing cycling for commuting. Based on these findings, several potential measures are demonstrated to policymakers and transportation planners to alleviate traffic congestion and develop sustainable transportation systems.

Influence of Social Constraints, Mobility Incentives, and Restrictions on Commuters’ Behavioral Intentions and Moral Obligation towards the Metro-Bus Service in Lahore

“…The transit service quality needs to meet the demand of potential travelers. A study revealed that traveler's age, gender, and marital status have an indirect influence on commuter's mode choice and the costs incurred due to number of transfer points and distance to the nearest transit stations are significant factors that hinder commuters from selecting the public transit system [31]. Fare reduction and other associated incentives are effective in promoting the use of public transportation [32].…”

An Intelligent Framework for Analyzing the Feasible Modes of Transportation in Metropolitan Cities: A Hybrid Multicriteria Approach

“…(2) Legislation: the traffic routing policies and the extent of stringent measures taken to administer the functioning of the unconventional modes of transport also influence vehicle mode choice. [20]. e ecological dimension is captured by the factor of environmental pollution [21].…”

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

“…All these strategies and decisions go through the identification of user preferences regarding communication routes, transportation methods, and consumption habits, among others. That is why the use of tools such as simulation, optimization, and machine learning, is a fundamental aid for the decision-making process [9]. Figure 1 shows the time evolution, since 2010, of the number of Scopus-indexed articles that contain in their title or abstract the combination of words "sustainable transportation" (or "sustainable transportation") and each of the following combinations: "machine learning" or "artificial intelligence", "optimization", "simulation", or "fuzzy".…”